NextDimensionAI ships safer, faster healthcare voice agents with Hamming

NextDimensionAI uses its proprietary simulation building system to create scenario-based test cases in Hamming. Hundreds of scenarios reflect real patient behavior, from straightforward appointment requests to incomplete information, pauses, repeated questions, and unexpected call patterns.

Coverage is not limited to clean, idealized flows. The team deliberately builds cases with long pauses, interrupted speech, and noisy environments, qualitative factors that manual calling could not capture consistently.

Engineers tag each test and run large batches in parallel. Instead of making calls one by one, they run an entire suite at once and immediately see:

• Which scenarios pass or fail
• How prompt changes affect LLM behavior
• How the agent handles variations in noise, pauses, or accents

Hamming runs are now part of the team's default engineering workflow.

NextDimensionAI also uses Hamming to benchmark configuration changes. Controlled tests measure the latency effect of:

• Different compute regions
• PSTN providers
• LLM temperature and response settings
• TTFW adjustments

Hamming runs hundreds of synthetic calls under controlled conditions, so the team can measure the impact of each change precisely. These tests found a configuration that reduced latency by 40%.

For example, the team measured how small fluctuations in TTFW affected patient perception, and how different PSTN carriers performed under load. The results also serve as historical reference points: when new models, telephony providers, or infrastructure environments become available, the team can quickly judge whether switching is worthwhile.

Internal QA tools and supervisor agents monitor production calls continuously. The QA loop combines automated evaluation with human-in-the-loop (HITL) review: supervisor agents label calls on intent accuracy, conversation quality, and completion outcomes.

When a call is flagged, a forward deployed AI engineer reviews the agent's performance, finds the root cause, and turns it into a reproducible Hamming test. Every improvement then goes through two steps:

• Run the new post-production test case
• Re-run all original pre-production tests

This keeps incremental improvements from breaking existing functionality, and the whole organization learns from every real failure. The result is a library of real-world cases: patients with strong accents, callers who give too much or too little information, prescription issues, and behavior outside any scripted flow. Each one is a test the agent must pass before any future release.

Because NextDimensionAI works in healthcare, HIPAA requirements are built into its test cases. The team writes scenarios that check explicitly whether an agent complies with HIPAA, and tests conversational edge cases such as callers trying to bypass verification by volunteering extra details, or asking the agent to read back medical history.

With Hamming in its workflow, NextDimensionAI moves quickly from design to production. Design, development, and testing share one consistent QA process, so the team can check behavior and ship updates with confidence. Its voice agents now reach 99% reliability on production calls.

Hamming is now part of NextDimensionAI's onboarding. The team runs live simulations of 50 simultaneous calls, giving customers a real-time view of how the agent performs inside their own AI platform.

As NextDimensionAI expands its product, Hamming lets the team test at scale without slowing development. The growing library of scenarios, benchmarks, and real-world cases means each deployment builds on the reliability of the last.